Personalized prediction of one-year mental health deterioration using adaptive learning algorithms: a multicenter breast cancer prospective study

Identifying individual patient characteristics that contribute to long-term mental health deterioration following diagnosis of breast cancer (BC) is critical in clinical practice. The present study employed a supervised machine learning pipeline to address this issue in a subset of data from a prospective, multinational cohort of women diagnosed with stage I–III BC with a curative treatment intention. Patients were classified as displaying stable HADS scores (Stable Group; n = 328) or reporting a significant increase in symptomatology between BC diagnosis and 12 months later (Deteriorated Group; n = 50). Sociodemographic, life-style, psychosocial, and medical variables collected on the first visit to their oncologist and three months later served as potential predictors of patient risk stratification. The flexible and comprehensive machine learning (ML) pipeline used entailed feature selection, model training, validation and testing. Model-agnostic analyses aided interpretation of model results at the variable- and patient-level. The two groups were discriminated with a high degree of accuracy (Area Under the Curve = 0.864) and a fair balance of sensitivity (0.85) and specificity (0.87). Both psychological (negative affect, certain coping with cancer reactions, lack of sense of control/positive expectations, and difficulties in regulating negative emotions) and biological variables (baseline percentage of neutrophils, thrombocyte count) emerged as important predictors of mental health deterioration in the long run. Personalized break-down profiles revealed the relative impact of specific variables toward successful model predictions for each patient. Identifying key risk factors for mental health deterioration is an essential first step toward prevention. Supervised ML models may guide clinical recommendations toward successful illness adaptation.


Measures
Outcome variable and patient grouping HADS employs a 4-point Likert scale to assess frequency of anxiety-and depression-related symptoms with acceptable internal consistency (Cronbach's = 0.90). Higher scores indicate more frequent psychological symptoms.

Predictors
Sociodemographic. The following variables were registered at baseline: Age (in years), education level (categorized as low [0-9 years] and high [>9 years]), relational status (alone, or with partner), children (yes, no), employment status (currently employed or not), type of employment (full-time, retired, or selfemployed vs. unemployed, housewife, or part-time employment), monthly income (very low vs average/high; adjusted for the Gross Domestic Product of home country of each participant). Two additional variables were aggregated over the first 3 months post diagnosis: sick leave taken (in days), and significant life stressors (other than BC-related) during the first three months post diagnosis (categorized as none/single event vs two or more events).
Life-style. The following variables were registered at baseline: Current smoker, alcohol consumption (no drinking or occasional consumption, defined as: ≤2 servings of beer and/or ≤1 servings of spirits per week, moderate, defined as: 3-6 servings of beer and/or ≤4 servings of spirits per week, heavy,

Psychosocial measures
Positive and Negative affect. The Positive and Negative Affectivity Schedule (PANAS) 3 was used to evaluate positive (10 adjectives; Cronbach's = 0.84) and negative affect (10 adjectives; Cronbach's = 0.75). A 5-point Likert type scale was adopted to assess affect over the past week. Higher scores represent higher levels of positive and negative affect, respectively.
Fear of Cancer Recurrence Inventory. The 9-item Fear of Cancer Recurrence Inventory (FCRI) questionnaire was used to measure the fear of a recurrence event 4 . Each item of the questionnaire is rated on a Likert type scale ranging from 0 ("not at all" or "never") to 4 ("a great deal" or "all the time"). The Social support and family resilience. The modified Medical Outcomes Study Social Support Survey (mMOS-SS) was used to assess social support, which has been shown to provide many benefits related to 5 overall health and well-being 10 . It consists of 8 items and the total score was calculated by summing all response values (Cronbach's α = 0.92). Higher total and subscale mMOS-SS scores reflect stronger social support. For the assessment of family resilience the Walsh Family Resilience Questionnaire 11 was used.
For the purposes of the BOUNCE study, two subscales were used: (i) communication and cohesion and (ii) perceived family coping. A higher total score indicates higher levels of family resilience.
Resilience as a personality characteristic (trait). The Connor-Davidson Resilience Scale was used to assess resilience as a trait (CD-RISC) 12 . The scale includes 10 items for quantifying the level of selfperceived resilience (e.g. ability to adapt to change; achieving my goals). Each item is rated on a 5-point Likert type scale from 0 ("not true at all") to 4 ("true nearly all the time") with higher total scores reflecting higher resilience levels (Cronbach's = 0.89).

Emotion regulation and relevant strategies. The Cognitive Emotion Regulation Questionnaire
(CERQ -short) was used to identify the cognitive emotion regulation strategies (or cognitive coping strategies) that BC patients followed when experiencing negative events or situations 13 . A 5-item Likert type scale was used for each item ranging from 1 ("(almost) never") to 5 ("(almost) always"). In addition, the Mindful Attention Awareness Scale (MAAS) 14 was used to assess the patients' characteristic of mindfulness. A total score is considered by summing all patients' responses with higher scores reflecting higher levels of dispositional mindfulness.
Other personality characteristics. Sense of coherence was assessed based on the Sense of Coherence (SOC)-13 questionnaire (Cronbach's α = 0.81, for the total score). Comprehensibility (5 items), manageability (4 items), and meaningfulness (4 items) were measured on a 7-point (Likert-type) response scale (from 1 (lower) to 7 (higher)) with higher total scores indicating higher level of sense of coherence.    Table S1. Model performance as a function of the imputation method used to address missing data.